Selecting a disconnect from different types of channel disconnects by training a machine learning module

ABSTRACT

Provided are techniques for selecting a disconnect by training a machine learning module. A machine learning module is provided that receives inputs and produces an output. The output produced from the machine learning module based on the inputs for the first I/O operation and an estimated amount of time to acquire resources for a first I/O operation is determined. An actual amount of time to acquire resources for the first I/O operation is determined. The machine learning module is retrained based on the inputs, the output, and the actual amount of time it took to acquire resources for the first I/O operation versus an estimated amount of time to acquire the resources for the first I/O operation. The retrained machine learning module is used to select one of disconnect from a channel, the logical disconnect from the channel, or the physical disconnect from the channel for a second I/O operation.

BACKGROUND 1. Field of the Invention

Embodiments of the invention relate to selecting a disconnect fromdifferent types of channel disconnects by training a machine learningmodule. In particular, embodiments of the invention relate to a storagecontroller selecting a disconnect from different types of channeldisconnects with reference to a channel that connects a host to thestorage controller by training a machine learning module.

2. Description of the Related Art

In certain storage system environments, a storage controller (or astorage controller complex) may comprise a plurality of storage serversthat are coupled to each other. The storage controller allows hostcomputing systems to perform Input/Output (I/O) operations with storagedevices controlled by the storage controller, where the host computingsystems may be referred to as hosts. A plurality of such storagecontrollers, hosts, and other computational devices may be deployed atone or more sites to provide an environment for storage and managementof data and also to provide an environment for data processing andrecovery.

A channel connects the storage controller to a host, and the host mayissue an I/O operation to the storage controller over the channel. Thechannel may be based upon a particular host attachment protocol, such asFibre Connection (FICON).

When an I/O operation comes into the storage controller via the channelfrom the host, the storage controller may not know how long it will taketo acquire resources to be able to process the I/O operation. While thestorage controller 120 is acquiring resources for the I/O operation, thestorage controller determines whether to disconnect from the host,perform a logical disconnect or perform a physical disconnect.

For example, the storage controller may determine not to disconnect fromthe channel when acquiring the resources (“I/O setup”) will take a smallamount of time (e.g., under 10 milliseconds (ms).

The storage controller may determine to perform a logical disconnectwhen acquiring the resources will take longer but not very long (e.g.,I/O setup will take between 10 to 500 ms). The I/O port logicallydisconnects from the host channel, for example, due to a cache miss(i.e., data is not available in the cache, and the exchange remainsopen). Data transfers for other I/O operations in progress may bemultiplexed over the I/O interface until the cache miss is resolved.FICON channels run on Fibre channel protocols that support framemultiplexing. I/O operations for different logical devices may beinterleaved on the I/O interface without disconnection. On FICONchannels, the channel closes the exchange when channel end status ispresented without device end. The overhead of establishing a newexchange on reconnection may be significant to overall performance andis avoided by logically disconnecting without presenting status.

The storage controller may determine to perform a physical disconnectwhen acquiring the resources will take longer than a threshold (e.g.,500 ms). In this case, the channel closes the exchange completely. Whenthe storage controller is ready to respond to the host, a newreconnection is established. The overhead of establishing a new exchangemay be costly.

In conventional systems, the storage controller takes a best guess onwhich option to choose for an I/O operation.

SUMMARY

Provided is a computer program product for selecting a disconnect fromdifferent types of channel disconnects by training a machine learningmodule. The computer program product comprises a computer readablestorage medium having program code embodied therewith, the program codeexecutable by at least one processor to perform operations. A machinelearning module is provided that receives inputs and produces an outputthat is used to select one of no disconnect from a channel, a logicaldisconnect from the channel, or a physical disconnect from the channelfor a first Input/Output (I/O) operation. The output produced from themachine learning module is determined based on the inputs for the firstI/O operation and an estimated amount of time to acquire resources forthe first I/O operation. An actual amount of time to acquire resourcesfor the first I/O operation is determined. The machine learning moduleis retrained based on the inputs, the output, and the actual amount oftime it took to acquire resources for the first I/O operation versus anestimated amount of time to acquire the resources for the first I/Ooperation. The retrained machine learning module is used to select oneof no disconnect from the channel, the logical disconnect from thechannel, or the physical disconnect from the channel for a second I/Ooperation.

Provided is a computer system for selecting a disconnect from differenttypes of channel disconnects by training a machine learning module. Thecomputer system comprises one or more processors, one or morecomputer-readable memories and one or more computer-readable, tangiblestorage devices; and program instructions, stored on at least one of theone or more computer-readable, tangible storage devices for execution byat least one of the one or more processors via at least one of the oneor more memories, to perform operations. A machine learning module isprovided that receives inputs and produces an output that is used toselect one of no disconnect from a channel, a logical disconnect fromthe channel, or a physical disconnect from the channel for a firstInput/Output (I/O) operation. The output produced from the machinelearning module is determined based on the inputs for the first I/Ooperation and an estimated amount of time to acquire resources for thefirst I/O operation. An actual amount of time to acquire resources forthe first I/O operation is determined. The machine learning module isretrained based on the inputs, the output, and the actual amount of timeit took to acquire resources for the first I/O operation versus anestimated amount of time to acquire the resources for the first I/Ooperation. The retrained machine learning module is used to select oneof no disconnect from the channel, the logical disconnect from thechannel, or the physical disconnect from the channel for a second I/Ooperation.

Provided is a computer-implemented method for selecting a disconnectfrom different types of channel disconnects by training a machinelearning module. The computer-implemented method comprises operations. Amachine learning module is provided that receives inputs and produces anoutput that is used to select one of no disconnect from a channel, alogical disconnect from the channel, or a physical disconnect from thechannel for a first Input/Output (I/O) operation. The output producedfrom the machine learning module is determined based on the inputs forthe first I/O operation and an estimated amount of time to acquireresources for the first I/O operation. An actual amount of time toacquire resources for the first I/O operation is determined. The machinelearning module is retrained based on the inputs, the output, and theactual amount of time it took to acquire resources for the first I/Ooperation versus an estimated amount of time to acquire the resourcesfor the first I/O operation. The retrained machine learning module isused to select one of no disconnect from the channel, the logicaldisconnect from the channel, or the physical disconnect from the channelfor a second I/O operation.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Referring now to the drawings in which like reference numbers representcorresponding parts throughout:

FIG. 1 illustrates, in a block diagram, a computing environment with astorage controller in accordance with certain embodiments.

FIG. 2 illustrates, in a block diagram, further details of a storagecontroller in accordance with certain embodiments.

FIG. 3 illustrates, in a block diagram, details of a machine learningmodule for selecting a disconnect from different types of channeldisconnects in accordance with certain embodiments.

FIG. 4 illustrates, in a block diagram, exemplary inputs to the machinelearning module, in accordance with certain embodiments.

FIG. 5 illustrates, in a block diagram, how output, from the machinelearning module, is mapped to one of no disconnect, logical disconnect,or physical disconnect in accordance with certain embodiments.

FIG. 6 illustrates an embodiment of a host request 600 in accordancewith certain embodiments.

FIG. 7 illustrates, in a flowchart, operations for initializing, using,and training a machine learning module in accordance with certainembodiments.

FIGS. 8A and 8B illustrate, in a flowchart, operations for processing anI/O operation in accordance with certain embodiments.

FIG. 9 illustrates, in a flowchart, operations for using a machinelearning module in accordance with certain embodiments.

FIG. 10 illustrates a computing architecture in which the components ofFIG. 1 may be implemented in accordance with certain embodiments.

DETAILED DESCRIPTION

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

Embodiments use a machine learning module (e.g., neural network) toenable the storage controller to more accurately determine whether todisconnect, perform a logical disconnect or perform a physicaldisconnect.

FIG. 1 illustrates, in a block diagram, a computing environment with astorage controller in accordance with certain embodiments. At least onehost 100 is coupled, via a channel 110, to a storage controller 120. Thehost 100 may submit Input/Output (I/O) operation requests to the storagecontroller (or storage control units) 120 over the channel 110 to accessdata at volumes 152 in storage 150. The volumes may be, for example,Logical Unit Numbers, Logical Devices, Logical Subsystems, etc. Thestorage 150 may be storage drives. With embodiments, the volumes are CKDvolumes.

In certain embodiments, the channel is based upon a particular hostattachment protocol, such as Fibre Connection (FICON), for example.

Communication software associated with the channel 110 includesinstructions and other software controlling communication protocols andthe operation of the communication hardware in accordance with thecommunication protocols, if any. It is appreciated that other channelprotocols may be utilized, depending upon the particular application.

Furthermore, as used herein, the term “unit of storage” or “storageunit” refers to a storage location containing one or more units of datastorage capable of storing one or more data units such as one or morevolumes, cylinders, tracks, segments, extents, or any portion thereof,or other unit or units of data suitable for transfer.

FIG. 2 illustrates, in a block diagram, further details of a storagecontroller 120 in accordance with certain embodiments. The storagecontroller 120 includes a Central Processing Unit (CPU) complex 222,including one or more processors or central processing units, eachhaving a single or multiple processor cores. In certain embodiments, aprocessor core contains the components of a CPU involved in executinginstructions, such as an Arithmetic Logic Unit (ALU), Floating PointUnit (FPU), and/or various levels of cache (such as L1 and L2 cache),for example. It is appreciated that a processor core may have otherlogic elements in addition to or instead of those mentioned herein.

The storage controller 120 communicates with the host 100 thru hostadapters 226. The CPU complex 222 communicates with the host adapters226 thru a bus and by using a mail queue 228.

Also, the storage controller 120 includes a memory 224. The CPU complex222 is connected to the memory 224.

The memory 224 includes a storage manager 230 for managing storageoperations (e.g., to store data in the storage 150 or retrieve data fromthe storage 150). The storage manager 230 includes a disconnect manager232, a cache 234, non-volatile storage (NVS) 236, and active tasks 238.The storage manager 230 also includes inputs 240, a machine learningmodule 242 (e.g., a neural network), and an output 244. The storagemanager 230 includes an I/O operation control structure 246 and queues250. The queues 250 include a cache wait queue 252 and an NVS queue 254.

With embodiments, the storage manager 230, including the disconnectmanager 232, is depicted as software stored in the memory 224 andexecuted by the CPU complex 222. However, it is appreciated that thelogic functions of the storage manager 230 may be implemented ashardware, software, firmware or combinations of one or more thereof,depending upon the particular application.

The machine learning module 242 implements a machine learning techniquesuch as decision tree learning, association rule learning, artificialneural network, inductive programming logic, support vector machines,Bayesian models, etc., to determine the output value 244.

In certain machine learning module 242 implementations, weights in ahidden layer of nodes may be assigned to these inputs to indicate theirpredictive quality in relation to other of the inputs based on trainingto reach the output value 244.

The machine learning module 242 may be trained using backwardpropagation to adjust weights at nodes in a hidden layer to produceadjusted output values based on the provided inputs 240. A margin oferror may be determined with respect to the actual output 244 from themachine learning module 224 and an expected output to train the machinelearning module 242 to produce the desired output value based on acalculated expected output. In backward propagation, the margin of errorof the output may be measured and the weights at nodes in the hiddenlayer may be adjusted accordingly to decrease the error.

Backward propagation may comprise a technique for supervised learning ofartificial neural networks using gradient descent. Given an artificialneural network and an error function, the technique may calculate thegradient of the error function with respect to the artificial neuralnetwork's weights.

The storage 150 may include volumes storing tracks. Tracks in volumes152 may be stored in cache 234 for fast access. As used herein, the termtrack may refer to a track of a disk storage unit, but may alsoreference to other units of data (or data units) configured in thestorage 150 such as a bit, byte, word, segment, page, block (such as aLogical Block Address (LBA)), etc., which may be a part of a largergrouping of data units, such as those stored collectively as a volume,logical device, etc. of data.

In certain embodiments, the storage 150 may be comprised of one or moresequential access storage devices, such as hard disk drives and magnetictape or may include non-sequential access storage devices, such as SolidState Drives (SSDs), for example. The storage 150 may comprise a singlesequential or non-sequential access storage device or may comprise anarray of storage devices, such as a Just a Bunch of Disks (JBOD), DirectAccess Storage Device (DASD), Redundant Array of Independent Disks(RAID) array, virtualization device, tape storage, flash memory, etc.

FIG. 3 illustrates, in a block diagram, details of a machine learningmodule 242 for selecting a disconnect from different types of channeldisconnects in accordance with certain embodiments.

The machine learning module 242 may comprise a neural network with acollection of nodes with links connecting them, where the links arereferred to as connections. For example, FIG. 3 shows a node 304connected by a connection 308 to the node 306. The collection of nodesmay be organized into three main parts: an input layer 310, one or morehidden layers 312, and an output layer 314.

The connection between one node and another is represented by a numbercalled a weight, where the weight may be either positive (if one nodeexcites another) or negative (if one node suppresses or inhibitsanother). Training the machine learning module 242 entails calibratingthe weights in the machine learning module 242 via mechanisms referredto as forward propagation 316 and backward propagation 322. Bias nodesthat are not connected to any previous layer may also be maintained inthe machine learning module 242. A bias may be described as an extrainput of 1 with a weight attached to it for a node.

In certain embodiments, the input data 318 . . . 320 are examples ofinputs 240, and output 324 is an example of output 244.

In forward propagation 316, a set of weights are applied to the inputdata 318 . . . 320 to calculate the output 324. For the first forwardpropagation, the set of weights may be selected randomly or set by, forexample, a system administrator.

In backward propagation 322 a measurement is made for a margin of errorof the output 324, and the weights are adjusted to decrease the error.Backward propagation 322 compares the output that the machine learningmodule 242 produces with the output that the machine learning module 242was meant to produce, and uses the difference between them to modify theweights of the connections between the nodes of the machine learningmodule 242, starting from the output layer 314 through the hidden layers312 to the input layer 310, i.e., going backward in the machine learningmodule 242. In time, backward propagation 322 causes the machinelearning module 242 to learn, reducing the difference between actual andintended output to the point where the two come very close or coincide.Thus, the machine learning module 242 is configured to repeat bothforward and backward propagation until the weights of the machinelearning module 242 are calibrated to accurately predict an output.

FIG. 4 illustrates, in a block diagram, exemplary inputs 240 to themachine learning module 242, in accordance with certain embodiments.

A cache wait queue length 402 describes a length of the cache wait queue252 that stores I/O operations waiting for cache segments. That is, theI/O operations are waiting for tracks that are not in the cache 234.

The NVS wait queue length overall 404 describes a length of the NVS waitqueue 254 that stores I/O operations waiting for NVS segments. That is,the I/O operations are waiting for tracks that are not in the NVS 236.

For write operations, there are ranks. The NVS wait queue for that rank406 describes the length of the NVS wait queue 254 for the rank to whichan I/O operation belongs.

The CPU utilization 408 describes a range from 0%-100% and indicates howmuch the CPU complex 222 is being utilized. With embodiments, this isthe current CPU utilization.

The number of active tasks 410 describes the number of active tasks 238for I/O operations from the host that are executing at the storagecontroller 120. With embodiments, the active tasks 238 are Task ControlBlocks (TCBs). The TCBs perform, for example, the operations to destagetracks from the cache 252 to the storage 150.

The mail queue length 412 describes a number of messages queued in themail queue 228. Different components may communicate via the mail queue228. For example, an active task 238 may leave a message in the mailqueue 228 for a host adapter 226. In certain embodiments, the mail queue228 stores the mail between the CPU complex 222 and the host adapters226. In additional embodiments, there may be other mail queues withinthe storage controller 120.

The number of Peer-to-Peer Remote Copy (PPRC) relations 414 describesthe number of this type of copy operation executing at the storagecontroller 120. Peer-to-Peer Remote Copy (PPRC) function supports theconcept of a PPRC consistency group. Volumes in a PPRC relationship thatare configured into a PPRC consistency group are maintained to ensurethat a group of updates made to volumes at the primary system are alsoeventually made on the volumes at the secondary system to maintain dataconsistency for those volumes of the group. Accordingly, consistencygroups may be formed between volumes of the primary system and thesecondary system which are consistent with respect to a particular setof updates or a particular point in time, notwithstanding the overallasynchronous relationship between the primary system and the secondarysystem.

The number of Extended Remote Copy (XRC) relations describes a number ofthis type of copy operation executing at the storage controller 120. XRCprovides an asynchronous remote copy solution.

The number of point-in-time copy relations 418 describes the number ofthis type of copy operation executing at the storage controller 120. Apoint-in-time copy function may be an IBM® FlashCopy® function, forexample. (IBM and FlashCopy are registered trademarks or common lawmarks of International Business Machines Corporation in the UnitedStates and/or other countries.) The point-in-time copy function createsa “snapshot” of the contents of a source volume as of a particularpoint-in-time in a target volume which may be referred to as thepoint-in-time copy volume. One version of a point-in-time copy functiontransfers the contents of the source volume to the point-in-time copyvolume in a background copy operation. The point-in-time copy functionmay also be referred to as a point-in-time snap copy function. Apoint-in-time copy may be described as a copy of the data consistent asof a particular point-in-time, and would not include updates to the datathat occur after the point-in-time.

The type of I/O operation 420 indicates whether the I/O operation is aread operation, a write operation, a susbsytem operation or amiscellaneous (e.g., reading configuration data) operation.

The Copy Services (CS) resource usage 422 indicates resource usage byCopy Services (e.g., low, medium or high). Copy Services supportscopying for the different types of relations. Copy Services may movedata between storage controllers and/or storage volumes.

The buffer usage 424 describes an amount of the buffers used for I/Ooperations. The I/O operations may read data from the buffers or writedata to the buffers.

For forward propagation, the machine learning module 242 takes theinputs 240 and generates the output 244. With embodiments, the values ofthe inputs 240 and the output 244 are saved in the I/O operation controlstructure 246 and used for backward propagation.

The machine learning module 242 is trained using backward propagation.In certain embodiments, the backward propagation may be done when an I/Ooperation completes or when a pre-determined number (N) I/O operationscomplete in the storage controller 120. At the completion of the Nth I/Ooperation, the machine learning module 242 is trained based on theactual amount of time it took to acquire resources in the storagecontroller 120 versus the value for the estimated amount of time toacquire the resources saved in the I/O operations control structure 246.

With embodiments, the margin of error is the estimated amount of time toacquire the resources (from the I/O operation control structure) minusthe actual amount of time taken for acquiring the resources (e.g.,estimated amount of time for acquiring resources—actual amount of timefor acquiring resources). This margin of error, along with the inputvalues saved in the I/O operations control structure 246, are backwardpropagated to adjust weights and margins in the machine learning module242.

FIG. 5 illustrates, in a block diagram, mappings 500 to show how output244, from the machine learning module 242, is mapped to one of nodisconnect, logical disconnect, or physical disconnect in accordancewith certain embodiments. The machine learning module 242 may output avalue in the range of 0 to 1. The disconnect manager 232 determines thatthere is to be no disconnect for an output 244 falling in a first range(e.g., 0 to 0.19). The disconnect manager 232 determines that there isto be a logical disconnect for an output falling in a second range(e.g., 0.2 to 0.49). The disconnect manager 232 determines that there isto be a physical disconnect for an output falling in a third range(e.g., 0.5 to 1).

FIG. 6 illustrates an embodiment of a host request 600 in accordancewith certain embodiments. The host request 600 may include a host ID602; a type of I/O operation 604; and a priority of the I/O operation606. With embodiments, the host request 600 may include otherinformation.

FIG. 7 illustrates, in a flowchart, operations for initializing, using,and training a machine learning module in accordance with certainembodiments. Control begins at block 700 with assignment of initialweights for the machine learning module 242 based on estimates. In block702, an initial training phase of the machine learning module 242 isperformed. In block 704, the machine learning module 242 is used toselect one of no disconnect, logical disconnect, or physical disconnectfrom a channel for at least one I/O operation. In block 706, whether toretrain the machine learning module is determined. In certainembodiments, retraining occurs after completion of an I/O operation orwhen a pre-determined number (“N”) of I/O operations complete in thestorage controller 120. If so, processing continues to block 708,otherwise, processing loops back to block 704. In block 708, the machinelearning module 242 is retrained, by adjusting the initial weights, toimprove the determination of whether to select one of no disconnect,logical disconnect, or physical disconnect.

FIGS. 8A and 8B illustrate, in a flowchart, operations for processing anI/O operation in accordance with certain embodiments. Control begins atblock 800 with the disconnect manager 232 receiving an I/O operationfrom a host on a channel. In block 802, the disconnect manager 232generates inputs to a machine learning module (such as inputs shown inFIG. 4). In certain embodiments, the inputs are a cache wait queuelength, an NVS wait queue length overall, an NVS wait queue length forthat rank, a CPU utilization, a number of active tasks, a mail queuelength, a number of copy relations for each of different copy relations(i.e., a number of PPRC relations, a number of XRC relations, and anumber of point-in-time copy relations), type of I/O operation, copyservices resource usage, and buffer usage.

In block 804, the disconnect manager 232 provides the inputs to themachine learning module 242. In block 806, the disconnect manager 232receives an output from the machine learning module 242.

In block 808, the disconnect manager 232 determines whether the outputfalls in a first range (e.g., 0 to 0.19). If so, processing continues toblock 810, otherwise, processing continues to block 812. In block 810,the disconnect manager 232 determines that no disconnect from thechannel is needed.

In block 812, the disconnect manager 232 determines whether the outputfalls in a second range (e.g., 0.2 to 0.49). If so, processing continuesto block 814, otherwise, processing continues to block 816. In block814, the disconnect manager 232 performs a logical disconnect from thechannel.

In block 816, the disconnect manager 232 performs a physical disconnectfrom the channel. If processing reaches block 816, then the output fallsin a third range (e.g., 0.5 to 1). From block 816 (FIG. 8A), processingcontinues to block 818 (FIG. 8B).

In block 818, the storage manager 230 acquires resources for the I/Ooperation. In block 820, the storage manager 230 stores an actual amountof time to acquire the resources for the I/O operation. In block 822,the disconnect manager 232 processes the I/O operation with the acquiredresources. In block 824, the storage manager 230, if needed, reconnectsto the channel. In block 826, the storage manager 230 returns a responseto the host (e.g., returns data that has been read, returns a statusindicator that indicates whether the I/O operation was completedsuccessfully, etc.).

FIG. 9 illustrates, in a flowchart, operations for using a machinelearning module in accordance with certain embodiments. Control beginsat block 900 with a machine learning module 242 being provided thatreceives inputs and produces an output that is used to select one ofdisconnect from a channel, logical disconnect from the channel, orphysical disconnect from the channel for a first I/O operation. In block902, the output produced from the machine learning module based on theinputs and an estimated amount of time to acquire resources for thefirst I/O operation is determined. In block 904, an actual amount oftime it took to acquire resources for the first I/O operation isdetermined. In block 906, in response to determining that it is time toretrain, retraining of the machine learning module is initiated. Incertain embodiment, it is determined that it is time to retrain when thefirst I/O operation completes or when a pre-determined number (N) of I/Ooperations complete in the storage controller 120. In block 908, themachine learning module 242 is retrained based on the inputs, theoutput, and the actual amount of time it took to acquire resources forthe first I/O operation versus an estimated amount of time to acquirethe resources for the first I/O operation (margin of error) to adjustweights in the machine learning module. In block 910, the retrainedmachine learning module 242 is used to select one of disconnect from achannel, logical disconnect from the channel, or physical disconnectfrom the channel for a second I/O operation.

With embodiments, the machine learning module 242 is a neural network,which may be described as a collection of “neurons” with “synapses”connecting them. The collection is organized into three main parts: aninput layer 310, one or more hidden layers 312, and an output layer 314.

With embodiments, there may be multiple hidden layers, with the term“deep” learning implying multiple hidden layers. Hidden layers may beuseful when the neural network has to make sense of somethingcomplicated, contextual, or non-obvious, such as image recognition. Theterm “deep” learning comes from having many hidden layers. These layersare known as “hidden”, since they are not visible as a network output.

Thus, with embodiments, training a neural network may be described ascalibrating all of the “weights” by repeating the forward propagation316 and the backward propagation 322.

Since neural networks are useful for regression, the best input data maybe numbers (as opposed to discrete values, such as colors or moviegenres, whose data is better for statistical classification models).

With embodiments, the output 324 is a number within a range (e.g., arange from 0 to 1, and this ultimately depends on the activationfunction).

In the forward propagation 316, embodiments apply a set of weights tothe input data 318 . . . 320 and calculate an output 324.

In backward propagation 322, embodiments measure the margin of error ofthe output and adjust the weights accordingly to decrease the error.

Neural networks repeat both forward and backward propagation until theweights are calibrated to accurately predict the output 324.

Embodiments optimize storage controller performance by using andtraining neural networks to assist the storage controller 120 indetermining whether to perform a logical or physical disconnection fromthe host 100. Embodiments use the machine learning module 242 to predicta wait time period needed for the storage controller 120 to complete itsrequired tasks and determine whether to remain connected to the host 100during the wait period. Embodiments use inputs 240 to estimate theamount of time needed to complete the outstanding tasks. Embodimentsfurther measures the actual amount of time elapsed to complete theoutstanding tasks, compare the actual amount of time to the estimatedamount of time, and adjust the machine learning module 242.

The reference characters used herein, such as n and r are used to denotea variable number of instances of an element, which may represent thesame or different values, and may represent the same or different valuewhen used with different or the same elements in different describedinstances.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

FIG. 10 illustrates a computing architecture in which the components ofFIG. 1 may be implemented in accordance with certain embodiments. Thecomputational components of FIG. 1, including the host 100 and thestorage controller 120 may implement computer architecture 1002.Computer system/server 1002 may be described in the general context ofcomputer system executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 1002 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 10, the computer system/server 1002 is shown in theform of a general-purpose computing device. The components of computersystem/server 1002 may include, but are not limited to, one or moreprocessors or processing units 1004, a system memory 1006, and a bus1008 that couples various system components including system memory 1006to processor 1004. Bus 1008 represents one or more of any of severaltypes of bus structures, including a memory bus or memory controller, aperipheral bus, an accelerated graphics port, and a processor or localbus using any of a variety of bus architectures. By way of example, andnot limitation, such architectures include Industry StandardArchitecture (ISA) bus, Micro Channel Architecture (MCA) bus, EnhancedISA (EISA) bus, Video Electronics Standards Association (VESA) localbus, and Peripheral Component Interconnects (PCI) bus.

Computer system/server 1002 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 1002, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 1006 can include computer system readable media in theform of volatile memory, such as random access memory (RAM) 1010 and/orcache memory 1012. Computer system/server 1002 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 1013 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 1008 by one or more datamedia interfaces. As will be further depicted and described below,memory 1006 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 1014, having a set (at least one) of program modules1016, may be stored in memory 1006 by way of example, and notlimitation, as well as an operating system, one or more applicationprograms, other program modules, and program data. Each of the operatingsystem, one or more application programs, other program modules, andprogram data or some combination thereof, may include an implementationof a networking environment. The components of the computer 1002 may beimplemented as program modules 1016 which generally carry out thefunctions and/or methodologies of embodiments of the invention asdescribed herein. The systems of FIG. 1 may be implemented in one ormore computer systems 1002, where if they are implemented in multiplecomputer systems 1002, then the computer systems may communicate over anetwork.

Computer system/server 1002 may also communicate with one or moreexternal devices 1018 such as a keyboard, a pointing device, a display1020, etc.; one or more devices that enable a user to interact withcomputer system/server 1002; and/or any devices (e.g., network card,modem, etc.) that enable computer system/server 1002 to communicate withone or more other computing devices. Such communication can occur viaInput/Output (I/O) interfaces 1022. Still yet, computer system/server1002 can communicate with one or more networks such as a local areanetwork (LAN), a general wide area network (WAN), and/or a publicnetwork (e.g., the Internet) via network adapter 1024. As depicted,network adapter 1024 communicates with the other components of computersystem/server 1002 via bus 1008. It should be understood that althoughnot shown, other hardware and/or software components could be used inconjunction with computer system/server 1002. Examples, include, but arenot limited to: microcode, device drivers, redundant processing units,external disk drive arrays, RAID systems, tape drives, and data archivalstorage systems, etc.

The terms “an embodiment”, “embodiment”, “embodiments”, “theembodiment”, “the embodiments”, “one or more embodiments”, “someembodiments”, and “one embodiment” mean “one or more (but not all)embodiments of the present invention(s)” unless expressly specifiedotherwise.

The terms “including”, “comprising”, “having” and variations thereofmean “including but not limited to”, unless expressly specifiedotherwise.

The enumerated listing of items does not imply that any or all of theitems are mutually exclusive, unless expressly specified otherwise.

The terms “a”, “an” and “the” mean “one or more”, unless expresslyspecified otherwise.

Devices that are in communication with each other need not be incontinuous communication with each other, unless expressly specifiedotherwise. In addition, devices that are in communication with eachother may communicate directly or indirectly through one or moreintermediaries.

A description of an embodiment with several components in communicationwith each other does not imply that all such components are required. Onthe contrary a variety of optional components are described toillustrate the wide variety of possible embodiments of the presentinvention.

When a single device or article is described herein, it will be readilyapparent that more than one device/article (whether or not theycooperate) may be used in place of a single device/article. Similarly,where more than one device or article is described herein (whether ornot they cooperate), it will be readily apparent that a singledevice/article may be used in place of the more than one device orarticle or a different number of devices/articles may be used instead ofthe shown number of devices or programs. The functionality and/or thefeatures of a device may be alternatively embodied by one or more otherdevices which are not explicitly described as having suchfunctionality/features. Thus, other embodiments of the present inventionneed not include the device itself.

The foregoing description of various embodiments of the invention hasbeen presented for the purposes of illustration and description. It isnot intended to be exhaustive or to limit the invention to the preciseform disclosed. Many modifications and variations are possible in lightof the above teaching. It is intended that the scope of the invention belimited not by this detailed description, but rather by the claimsappended hereto. The above specification, examples and data provide acomplete description of the manufacture and use of the composition ofthe invention. Since many embodiments of the invention can be madewithout departing from the spirit and scope of the invention, theinvention resides in the claims herein after appended.

What is claimed is:
 1. A computer program product, the computer programproduct comprising a computer readable storage medium having programcode embodied therewith, the program code executable by at least oneprocessor to perform operations, the operations comprising: providing amachine learning module that receives inputs and produces an output thatis used to select one of no disconnect from a channel, a logicaldisconnect from the channel, or a physical disconnect from the channelfor a first Input/Output (I/O) operation; determining the outputproduced from the machine learning module based on the inputs for thefirst I/O operation and an estimated amount of time to acquire resourcesfor the first I/O operation; determining an actual amount of time toacquire resources for the first I/O operation; retraining the machinelearning module based on the inputs, the output, and the actual amountof time it took to acquire resources for the first I/O operation versusthe estimated amount of time to acquire the resources for the first I/Ooperation; and using the retrained machine learning module to select oneof no disconnect from the channel, the logical disconnect from thechannel, or the physical disconnect from the channel for a second I/Ooperation.
 2. The computer program product of claim 1, wherein themachine learning module is retrained in response to one or more I/Ooperations being completed.
 3. The computer program product of claim 1,wherein a margin of error is determined based on the estimated amount oftime it took to acquire the resources and the actual amount of time ittook to acquire resources, and wherein the margin of error is used toretrain the machine learning module.
 4. The computer program product ofclaim 1, wherein weights are assigned to the machine learning module,and wherein the weights are adjusted when retraining the machinelearning module.
 5. The computer program product of claim 1, wherein theprogram code is executable by the at least one processor to perform: inresponse to the output falling into a first range, determining that nodisconnect from the channel is to be performed; in response to theoutput falling into a second range, performing the logical disconnect;and in response to the output falling into a third range, performing thephysical disconnect.
 6. The computer program product of claim 1, whereinthe inputs comprise a cache wait queue length, a Non-Volatile Storage(NVS) wait queue length overall, an NVS wait queue length for a rank, aCentral Processing Unit (CPU) utilization, a number of active tasks, amail queue length, a number of copy relations for different copyrelations, a type of I/O operation, a copy services resource usage, anda buffer usage.
 7. A computer system, comprising: one or moreprocessors, one or more computer-readable memories and one or morecomputer-readable, tangible storage devices; and program instructions,stored on at least one of the one or more computer-readable, tangiblestorage devices for execution by at least one of the one or moreprocessors via at least one of the one or more computer-readablememories, to perform operations comprising: providing a machine learningmodule that receives inputs and produces an output that is used toselect one of no disconnect from a channel, a logical disconnect fromthe channel, or a physical disconnect from the channel for a firstInput/Output (I/O) operation; determining the output produced from themachine learning module based on the inputs for the first I/O operationand an estimated amount of time to acquire resources for the first I/Ooperation; determining an actual amount of time to acquire resources forthe first I/O operation; retraining the machine learning module based onthe inputs, the output, and the actual amount of time it took to acquireresources for the first I/O operation versus the estimated amount oftime to acquire the resources for the first I/O operation; and using theretrained machine learning module to select one of no disconnect fromthe channel, the logical disconnect from the channel, or the physicaldisconnect from the channel for a second I/O operation.
 8. The computersystem of claim 7, wherein the machine learning module is retrained inresponse to one or more I/O operations being completed.
 9. The computersystem of claim 7, wherein a margin of error is determined based on theestimated amount of time it took to acquire the resources and the actualamount of time it took to acquire resources, and wherein the margin oferror is used to retrain the machine learning module.
 10. The computersystem of claim 7, wherein weights are assigned to the machine learningmodule, and wherein the weights are adjusted when retraining the machinelearning module.
 11. The computer system of claim 7, wherein theoperations further comprise: in response to the output falling into afirst range, determining that no disconnect from the channel is to beperformed; in response to the output falling into a second range,performing the logical disconnect; and in response to the output fallinginto a third range, performing the physical disconnect.
 12. The computersystem of claim 7, wherein the inputs comprise a cache wait queuelength, a Non-Volatile Storage (NVS) wait queue length overall, an NVSwait queue length for a rank, a Central Processing Unit (CPU)utilization, a number of active tasks, a mail queue length, a number ofcopy relations for different copy relations, a type of I/O operation, acopy services resource usage, and a buffer usage.
 13. Acomputer-implemented method, comprising: providing a machine learningmodule that receives inputs and produces an output that is used toselect one of no disconnect from a channel, a logical disconnect fromthe channel, or a physical disconnect from the channel for a firstInput/Output (I/O) operation; determining the output produced from themachine learning module based on the inputs for the first I/O operationand an estimated amount of time to acquire resources for the first I/Ooperation; determining an actual amount of time to acquire resources forthe first I/O operation; retraining the machine learning module based onthe inputs, the output, and the actual amount of time it took to acquireresources for the first I/O operation versus the estimated amount oftime to acquire the resources for the first I/O operation; and using theretrained machine learning module to select one of no disconnect fromthe channel, the logical disconnect from the channel, or the physicaldisconnect from the channel for a second I/O operation.
 14. Thecomputer-implemented method of claim 13, wherein the machine learningmodule is retrained in response to one or more I/O operations beingcompleted.
 15. The computer-implemented method of claim 13, wherein amargin of error is determined based on the estimated amount of time ittook to acquire the resources and the actual amount of time it took toacquire resources, and wherein the margin of error is used to retrainthe machine learning module.
 16. The computer-implemented method ofclaim 13, wherein weights are assigned to the machine learning module,and wherein the weights are adjusted when retraining the machinelearning module.
 17. The computer-implemented method of claim 13,further comprising: in response to the output falling into a firstrange, determining that no disconnect from the channel is to beperformed; in response to the output falling into a second range,performing the logical disconnect; and in response to the output fallinginto a third range, performing the physical disconnect.
 18. Thecomputer-implemented method of claim 13, wherein the inputs comprise acache wait queue length, a Non-Volatile Storage (NVS) wait queue lengthoverall, an NVS wait queue length for a rank, a Central Processing Unit(CPU) utilization, a number of active tasks, a mail queue length, anumber of copy relations for different copy relations, a type of I/Ooperation, a copy services resource usage, and a buffer usage.